Most types of mobile devices include at least one camera that faces the user, typically referred to as a front-facing camera. Front-facing cameras may be used for applications such as videoconferencing and gaze tracking, for example. However, the optics used in front-facing cameras have many limitations.
One cause of these limitations is the depth of the form factors used to house the optics. For example, in a mobile phone, the front-facing camera, and any primary rear-facing cameras, are typically disposed along the z-axis (depth) of the phone. As the trend is toward thinner phones, the depth of the phone casing limits the length of the camera. This size limitation results in front-facing cameras having a very small focal length, which in turn limits the aperture (light capture). Other limitations of front-facing cameras include having a wide field of view, a fixed focal point of approximately 600 mm to 800 mm, and a smaller, lower resolution image sensor than the primary rear-facing camera. The lens quality on front-facing cameras is also subpar compared with rear-facing cameras, which may result in artifacts in the images.
The result of these limitations is that front-facing cameras pose challenges for mobile device applications that require images of a certain quality, such as iris recognition for biometric authentication and gaze tracking, for example.
Iris recognition uses mathematical pattern recognition techniques on video images of a user's iris, which has unique random complex patterns. Iris recognition algorithms need images that are sufficiently high resolution and have enough contrast to distinguish the patterns in the iris.
Many iris scanning applications have been built and deployed, typically for purposes of access control or flow control at large facilities such as airports, for example. Iris recognition requires infrared light because the structures of the Iris are not visible in visible light. Therefore, iris recognition systems require an infrared illumination source and an image sensor capable of detecting infrared light. These installations typically have high quality stationary imagers having complex and expensive optics and large image sensors capable of taking images and scanning irises of individuals at distances up to 2 m.
Implementing such an iris-based authentication system in a mobile device proves challenging due to the problems described above with front-facing cameras, such as small focal length, limited aperture, and a wide field of view. Attempting to capture an image of an iris with a wide field of view could result in the image capturing the user's iris in only a couple of pixels, i.e., with insufficient resolution of details The fixed focal point, the low resolution image sensor and the subpar lens quality on front-facing cameras may also result in low quality, noisy images having artifacts. Furthermore, the phone is a moving object so there may be problems with motion blur, resulting in a reduction in image sharpness and contrast. The problem with motion blur may be compounded by the fact that the user has to manually aim the front-facing camera of the phone at the user's eye. The above problems may cause current iris recognition algorithms to fail if the user's eye is placed further than approximately five to eight inches from the mobile device. However, due to the fixed focal length the front-facing camera, the resulting image of the user′ eye at such a distance might be blurry due to the fixed focal length.
An additional problem is that because Iris recognition requires the use of infrared light, an issue with power may arise because an infrared light source could be a significant drain on the mobile device battery. The infrared light source could also be a health and safety issue since the user would be required to place his/her eye very close to the light source.
Accordingly, what is needed is an improved optic systems for mobile devices, particularly for use with applications that utilize front-facing cameras.